Text to SQL: Amazon Redshift Serverless for Generative SQL in Amazon Q
Text to SQL: Amazon Redshift Serverless for Generative SQL in Amazon Q
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 49m | 418 MB
Instructor: Wendy Wong
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 49m | 418 MB
Instructor: Wendy Wong
Are you ready to extract data and gain faster insights? In this course, learn how to enable text to SQL with AI-powered assistant Amazon Q in Amazon Redshift Query Editor to generate queries with prompt engineering and retrieval-augemented generation (RAG). Instructor Wendy Wong takes you through the architecture of Amazon Q to help you identify table schemas from your data and redefine the heavy lifting of figuring out table relationships. Along the way, learn to use prompts in natural language to produce customized SQL. By the end of this course, you’ll be prepared to use Amazon Q to help you to troubleshoot coding errors and regenerate code that you can run in SQL Notebook.
Learning objectives
- Discover and extract data by using natural language to generate SQL queries.
- Create intermediate- and advanced-level SQL queries and rerun them directly from SQL Notebook.
- Identify and debug SQL errors using the generative SQL console.
- Explain the benefits of using Amazon Q generative SQL in Amazon Redshift Query Editor. Describe the solution architecture of how SQL queries are generated based on context with prompt engineering and retrieval augmented generation (RAG).
Text to SQL: Amazon Redshift Serverless for Generative SQL in Amazon Q
